Why the dot function/method is slower than @ on python 3.5.1? Tested from the
latest 1.11 maintenance branch.
np.__version__
Out[39]: '1.11.0.dev0+Unknown'
%timeit A @ c
10000 loops, best of 3: 185 µs per loop
%timeit A.dot(c)
1000 loops, best of 3: 526 µs per loop
%timeit np.dot(A,c)
1000 loops, best of 3: 527 µs per loop
A.dtype, A.shape, A.flags
Out[43]:
(dtype('float32'), (100, 100, 3), C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False)
c.dtype, c.shape, c.flags
Out[44]:
(dtype('float32'), (3, 3), C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
UPDATEIFCOPY : False)
From: NumPy-Discussion <[email protected]> on behalf of
Charles R Harris <[email protected]>
Sent: 26 January 2016 22:49
To: numpy-discussion; SciPy Developers List; SciPy Users List
Subject: [Numpy-discussion] Numpy 1.11.0b1 is out
Hi All,
I'm pleased to announce that Numpy 1.11.0b1 is now available on sourceforge.
This is a source release as the mingw32 toolchain is broken. Please test it out
and report any errors that you discover. Hopefully we can do better with 1.11.0
than we did with 1.10.0 ;)
Chuck
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